GraphAgent: Agentic Graph Language Assistant
Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuxuan Liang,, Chao Huang

TL;DR
GraphAgent is an automated agent pipeline that combines graph generation, task planning, and execution to handle complex semantic dependencies in data for predictive and generative tasks, demonstrating effectiveness across diverse datasets.
Contribution
We introduce GraphAgent, a novel integrated framework that automates graph construction, task planning, and execution for complex data analysis tasks using language and graph models.
Findings
Effective across various graph-related tasks
Automates complex semantic dependency handling
Open-source implementation available
Abstract
Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this work, we propose GraphAgent, an automated agent pipeline that addresses both explicit graph dependencies and implicit graph-enhanced semantic inter-dependencies, aligning with practical data scenarios for predictive tasks (e.g., node classification) and generative tasks (e.g., text generation). GraphAgent comprises three key components: (i) a Graph Generator Agent that builds knowledge graphs to reflect complex semantic dependencies; (ii) a Task Planning Agent that interprets diverse user queries and formulates corresponding tasks…
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Taxonomy
TopicsSemantic Web and Ontologies · Multi-Agent Systems and Negotiation · Service-Oriented Architecture and Web Services
